langgraph vs letta
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
lettaopen-source
Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.
Metrics
| langgraph | letta | |
|---|---|---|
| Stars | 28.0k | 21.8k |
| Star velocity /mo | 2.5k | 367.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8081963872278098 | 0.7466815258314535 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +Advanced persistent memory system that allows agents to learn and improve over time across sessions
- +Dual deployment options with both local CLI tool and cloud API for different use cases and security requirements
- +Model-agnostic architecture supporting multiple LLM providers with extensive SDK support for TypeScript and Python
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -Requires Node.js 18+ for CLI usage, which may limit adoption in some environments
- -API-based functionality requires API keys and cloud dependency for full feature access
- -As a relatively new platform for stateful agents, may have a learning curve for developers new to persistent memory concepts
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •Building coding assistants that remember project context and learn from previous debugging sessions
- •Creating customer support agents that maintain conversation history and learn customer preferences over time
- •Developing personal AI assistants that evolve their responses based on user behavior patterns and feedback